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检索条件"任意字段=Conference on Image Processing - Algorithms and Systems VI"

共 15618 条记录，以下是851-860 订阅

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GA-based Parameter Optimization of image processing for Contamination Inspection of Nonwoven Fabrics 48

GA-based Parameter Optimization of Image Processing for Cont...

引用

48th Annual conference of the IEEE Industrial Electronics Society, IECON 2022

作者： Kumazawa, Nobuhiko Miyazaki, Sota Hatta, Yoshiyuki Sato, Junya Ito, Kazuaki Otsuka, Yukio Kitagawa, Ryota Iwata, Kenji Hirayu, Hidekazu Gifu University Department of Mechanical Engineering Gifu Japan Gifu University Intelligent Production Technology R & D Center for Aerospace Gifu Japan Otsuka Corporation Hashima Japan Gifu Prefectural Industrial Technology Center Seki Japan

ISBN: (数字)9781665480253

ISBN: (纸本)9781665480253

The paper proposes the parameter optimization of image processing for contamination inspection of nonwoven fabrics. Currently, the automation of contamination inspection using image processing systems is being considered. In image processing, it is important to set the optimal parameters for the processing. However, it is necessary to search it from many combinations because there are some parameters. The proposed method searches for the optimal parameters based on a genetic algorithm. It reduces the search time in comparison with the conventional method. The paper indicates the effectiveness of the proposed method with the experimental results. © 2022 IEEE.

关键词： Genetic algorithms

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A study on Generative Adversarial Text to image Synthesis

A study on Generative Adversarial Text to Image Synthesis

引用

2023 International conference on Electrical, Computer, Communications and Mechatronics Engineering, ICECCME 2023

作者： Tahmid, Marjana Alam, Samiul Akram, Kalim Asif Ashrafi, Kazi Muhammad East Delta University Dept. of Cse Chittagong Bangladesh East Delta University Dept. of Eee Chittagong Bangladesh Ulm University Dept. of Cse Ulm Germany

ISBN: (纸本)9798350322972

image captioning is one of the most prevalent and difficult challenges in Natural Language processing and Computer vision: given an image, a written description of the image must be developed. The counterpart of the text to image problem is text to image synthesis, which involves creating a picture from a written description. These challenges approximate language translation challenges from a high-level perspective. images and text are two separate "languages"to convey related information, just like equivalent semantics might be represented in two different languages. This is undoubtedly beneficial, however existing AI systems are a long way from achieving this. Strong neural network topologies, such GANs (Generative Adversarial Networks), have been found to provide effective outcomes in recent years. There are, however, some text to image synthesis algorithms that use GAN (Generative Adversarial Network) that attempt to directly map words and characters to image pixels utilizing image synthesis and natural language synthesis approaches. To reconcile these advancements in text as well as image modeling and successfully translate visual representations from characters to pixels, we design an innovative deep architecture and GAN formulation in this study. Here, the demonstration of our model has been done by generating plausible images of birds and flowers from detailed text descriptions. © 2023 IEEE.

关键词： Generative adversarial networks

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A Comprehensive Review of Low-light image Enhancement Techniques

A Comprehensive Review of Low-light Image Enhancement Techni...

引用

image processing, Computer vision and Machine Learning (ICICML), International conference on

作者： Tian Gao Peilin Tao AMS PLA Systems Engineering Institute Beijing China

ISBN: (数字)9798350355413

ISBN: (纸本)9798350355420

Enhancing low-light images is a crucial research topic in computer vision, aiming at revealing image details hidden in the darkness and thus recovering images with normal lighting and color. However, there is a large variety of solutions proposed for this problem, and it is not a simple problem for researchers to systematically understand all types of solutions. This paper provides a comprehensive review of various techniques for low-light image enhancement (LLIE), including algorithms, datasets, evaluation metrics, and more. First, we categorize these methods into two broad directions based on the principles of the algorithms and provide a detailed introduction to each. These two directions are further categorized into four subcategories based on their learning approaches. Then we introduce the principles, characteristics, and current dilemmas and challenges of various methods in detail based on each classification. Finally, we discuss the more promising research directions in the future in light of the latest research progress.

关键词： Learning systems Deep learning Measurement Computer vision Reviews image color analysis Lighting Classification algorithms image enhancement Faces

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PhysGNN: A Physics-Driven Graph Neural Network Based Model for Predicting Soft Tissue Deformation in image-Guided Neurosurgery 36

PhysGNN: A Physics-Driven Graph Neural Network Based Model f...

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Salehi, Yasmin Giannacopoulos, Dennis McGill Univ Dept Elect & Comp Engn Montreal PQ Canada

ISBN: (纸本)9781713871088

Correctly capturing intraoperative brain shift in image-guided neurosurgical procedures is a critical task for aligning preoperative data with intraoperative geometry for ensuring accurate surgical navigation. While the finite element method (FEM) is a proven technique to effectively approximate soft tissue deformation through biomechanical formulations, their degree of success boils down to a trade-off between accuracy and speed. To circumvent this problem, the most recent works in this domain have proposed leveraging data-driven models obtained by training various machine learning algorithms-e.g., random forests, artificial neural networks (ANNs)-with the results of finite element analysis (FEA) to speed up tissue deformation approximations by prediction. These methods, however, do not account for the structure of the finite element (FE) mesh during training that provides information on node connectivities as well as the distance between them, which can aid with approximating tissue deformation based on the proximity of force load points with the rest of the mesh nodes. Therefore, this work proposes a novel framework, PhysGNN, a data-driven model that approximates the solution of the FEM by leveraging graph neural networks (GNNs), which are capable of accounting for the mesh structural information and inductive learning over unstructured grids and complex topological structures. Empirically, we demonstrate that the proposed architecture, PhysGNN, promises accurate and fast soft tissue deformation approximations, and is competitive with the state-of-the-art (SOTA) algorithms while promising enhanced computational feasibility, therefore suitable for neurosurgical settings.

关键词： Finite element method

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ShuffleMixer: An Efficient ConvNet for image Super-Resolution 36

ShuffleMixer: An Efficient ConvNet for Image Super-Resolutio...

引用

36th conference on Neural Information processing systems (NeurIPS)

作者： Sun, Long Pan, Jinshan Tang, Jinhui Nanjing Univ Sci & Technol Nanjing Peoples R China

ISBN: (纸本)9781713871088

Lightweight and efficiency are critical drivers for the practical application of image super-resolution (SR) algorithms. We propose a simple and effective approach, ShuffleMixer, for lightweight image super-resolution that explores large convolution and channel split-shuffle operation. In contrast to previous SR models that simply stack multiple small kernel convolutions or complex operators to learn representations, we explore a large kernel ConvNet for mobile-friendly SR design. Specifically, we develop a large depth-wise convolution and two projection layers based on channel splitting and shuffling as the basic component to mix features efficiently. Since the contexts of natural images are strongly locally correlated, using large depth-wise convolutions only is insufficient to reconstruct fine details. To overcome this problem while maintaining the efficiency of the proposed module, we introduce Fused-MBConvs into the proposed network to model the local connectivity of different features. Experimental results demonstrate that the proposed ShuffleMixer is about 3x smaller than the state-of-the-art efficient SR methods, e.g. CARN, in terms of model parameters and FLOPs while achieving competitive performance. The code is available at https://***/sunny2109/ShuffleMixer.

关键词： Convolution

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Machine Learning Model for Glass type Prediction 3

Machine Learning Model for Glass type Prediction

引用

3rd International conference for Advancement in Technology, ICONAT 2024

作者： Logeswaran, K. Savitha, S. Suresh, P. vimaladevi, M. Saliniyan, P. Sanjay, A. Yeswanth, A. Kongu Engineering College Department of Artificial Intelligence Tamilnadu Erode638060 India K.S.R College of Engineering Departmen of CSE Tamilnadu Tiruchengode637215 India Vellore Institute of Technology School of Computer Science and Engineering Department of Database Systems Tamilnadu Vellore632014 India

ISBN: (纸本)9798350354171

Successful operations in industries such as recycling, manufacturing and quality control depend on identifying and classifying different types of glass. Traditional methods, including manual analysis and traditional image processing methods, often suffer from errors and inconsistencies due to human factors and variations. This paper introduces a new automatic glass type identification system that uses advanced learning techniques, specifically K-Nearest Neighbors (KNN), CNN,RNN. By applying these algorithms, it is aimed to increase the accuracy and performance of the system's glass type classification. Next, we delve into the design and implementation of machine learning models by discussing data collection techniques, feature extraction technology, and training methods. A comparative analysis of the performance of KNN, RNN, Naive Bayes, Logistic Regression, Random Forest, Feed Forward Network, RNN, ANN, CNN. is performed and their accuracy, performance, and robustness are evaluated in different situations. It shows the most accurate and reliable glass type classification among traditional methods. This study also explores the benefits of using such systems in the manufacturing industry and shows how work efficiency can be increased and the number of errors reduced. © 2024 IEEE.

关键词： Logistic regression

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Domain Adaptive Recognition of Defects for Ground Penetrating Radar images 8

Domain Adaptive Recognition of Defects for Ground Penetratin...

引用

8th International conference on Signal and image processing, ICSIP 2023

作者： Huang, Zhe Jia, Yong Fan, Xiaodong Tang, Likun Chengdu University of Technology School of Electrical and Mechanical Engineering Sichuan Chengdu China

ISBN: (纸本)9798350397932

GPR technology is of significant importance for road maintenance authorities to detect road defect under the road surface, for it helps to monitor the construction process and detect defects early, thus reducing maintenance costs. The high cost of data collection and core verification for underground defects makes the dataset very valuable. In practical applications, different radar types and imaging methods often lead to heterogeneous generated radar images. In addition, there are significant differences between training data and real-world test data, which may lead to ambiguous predictions. This is because as the discrepance in different data domains increase, the transferability of the features extracted by neural networks significantly decreases at higher levels. Therefore, it is important to reduce dataset bias and enhance transferability in specific task layers. This article specifically designs a feature transfer enhancement algorithm for heterogeneous ground penetrating radar data based on deep domain adaptation. Compared with other deep domain adaptation algorithms such as DAN, DANN, and DeepCoral, the proposed method has the best recognition performance on underground defect data of ground penetrating radar. © 2023 IEEE.

关键词： Ground penetrating radar systems

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image Recognition and Enhancement Using Multi-Scale Retinex and Histogram Equalization

Image Recognition and Enhancement Using Multi-Scale Retinex ...

引用

2023 IEEE International conference on Intelligent and Innovative Technologies in Computing, Electrical and Electronics, ICIITCEE 2023

作者： Senthil Kumar, R. Rajalakhmi, B. Geethika, Palle Indrani, C. Kolalapudi, Ramya Swathi CSE NHCE Bangalore India

ISBN: (纸本)9781665462631

Nowadays, the need for a safe and at ease device is favoured by using every character in society. A fee-effective system is needed for Aerial surveillance systems capable of enhancing situational focus in the course of a couple of scales of place and time. With the assistance of an aerial surveillance machine, we are capable of accumulating a device that could report unmanned vehicles and humans. With improvements in technology, Unmanned motors and optical sensors are being fast produced with powerful usage of resources, therefore, it has ended up being less complicated to get images from the UAV at a low price. But the selection of those pixy received is reduced because of climatic conditions like fog, rain, harsh sunlight, etc. So, we advocate a photograph processing technique as a manner to use algorithms that include single-scale retinex, Multiscale retinex, histogram equalization, and colour recuperation to grow the fine of photographs. These select images have a higher evaluation, and subsequently can be used for better item detection. 1. The program can determine the objects without getting observed.2. The program can find multiple items. Three. MSRP for similar processing like histogram equalization and item detection to get the exceptional possible output © 2023 IEEE.

关键词： image enhancement

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FPGA Implementation of Various Division algorithms for image processing Applications - A Comparative Analysis

FPGA Implementation of Various Division Algorithms for Image...

引用

Signal processing, Computation, Electronics, Power and Telecommunication (IConSCEPT), International conference on

作者： Jayanthi B L S Kumar A Someshwaran M Sandya Nandhini Bharadwaj Department of ECE National Institute of Technology Puducherry Karaikal India

ISBN: (数字)9798331540685

ISBN: (纸本)9798331540692

Division is one of the most commonly sort after algorithm for performing image processing operations such as normalization, filtering, enhancement, deconvolution etc. Hence, the design of efficient division algorithm is highly essential in order to obtain a better image processing algorithm The first and foremost step in the implementation of any algorithm in hardware is to identify the number systems used to represent the inputs and outputs. The inputs, outputs and complexity of the algorithm varies based on the number system used. This paper proposes a recursive subtraction based fixed-point division algorithm. This work also provides a comparative analysis on various aspects such as area, power, delay and throughput, on comparing the proposed recursive subtraction based fixed-point division algorithm with the existing floating-point division, restoring integer division and non-restoring integer division algorithms and operator based fixed-point division algorithm.

关键词： Deconvolution Filtering Signal processing algorithms Signal processing Throughput image restoration Delays

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A Flexible Job Shop Scheduling Problem Involving Reconfigurable Machine Tools Under Industry 5.0 43rd

A Flexible Job Shop Scheduling Problem Involving Reconfigura...

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43rd IFIP WG 5.7 International conference on Advances in Production Management systems (APMS)

作者： Bakhshi-Khaniki, Hessam Tavakkoli-Moghaddam, Reza Hanzalek, Zdenek Vahedi-Nouri, Behdin Univ Tehran Coll Engn Sch Ind Engn Tehran Iran Czech Tech Univ Czech Inst Informat Robot & Cybernet Ind Informat Dept Prague Czech Republic

ISBN: (纸本)9783031716478;9783031716454;9783031716447

The rise of Industry 5.0 has introduced new demands for manufacturing companies, requiring a shift in how production schedules are managed to address human-centered, environmental, and economic goals comprehensively. The flexible job shop scheduling problem (FJSSP), which involves processing operations on various capable machines, accurately reflects the complexities of modern manufacturing settings. This paper investigates the FJSSP involving reconfigurable machine tools with configuration-dependent setup times, while integrating human aspects like worker assignments, moving time, and rest periods, as well as minimizing total energy consumption. A mixed-integer programming (MIP) model is developed to simultaneously optimize these objectives. The model determines the assignment of operations to machines, workers, and configurations while sequencing operations, scheduling worker movements, and respecting rest periods, and minimizing overall energy consumption. Given the NP-hard nature of the FJSSP with worker assignments and reconfigurable tools, a memetic algorithm (MA) is proposed. This meta-heuristic evolutionary algorithm features a three-layer chromosome encoding method, specialized crossover and mutation strategies, and neighborhood search mechanisms to enhance solution quality and diversity. Comparisons of MA with MIP and genetic algorithms (GA) on benchmark instances demonstrate the MA's efficiency and effectiveness, particularly for larger problem instances where MIP becomes impractical. This research paves the way for sustainable and resilient production schedules tailored for the factory of the future under the Industry 5.0 paradigm. The work bridges a crucial gap in current literature by integrating worker and environmental impact into the FJSSP with reconfigurable machine models.

关键词： Flexible Job Shop Scheduling Reconfigurable Manufacturing systems Human Centric Sustainable Manufacturing Industry 5.0

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